Is your data working for you or is it the other way around? A Q&A with Eagle’s Paul McInnis ahead of his panel at ENGAGE18 can help answer that question

Q: As it relates to data management, what are the biggest obstacles that stand in the way of whether data is working for or against the larger organization?

A: First and foremost, the biggest obstacle for many asset managers today are their legacy systems. That’s why we’re seeing so many organizations embarking on transformation initiatives that begin the journey by addressing the technology debt accumulated over the past 15 or 20 years. As old technology gets shuttled out, the adoption of agile and scalable systems allows organizations to store and process more data than ever before. And these efforts enable firms to not only leverage their data today but also positions them to seamlessly build out their capabilities in the future.

As digital information expands, the amount of data is increasing at a significant pace each year. Older systems simply can’t handle the volume or velocity of information. This glut, beyond exposing inadequate systems and degrading data quality, has also driven an emphasis on enterprise data management—it’s no longer just data management. Ten years ago, the amount and types of data being utilized was managed in different silos; today, that’s almost certainly a disaster waiting to happen and the impact is felt across the business.

Q: So as the philosophy of CIOs evolve, how has this changed how organizations manage data?

A: Enterprise data management cannot exist without data governance, which establishes a rigid policy and operating framework. Investment institutions are beginning to understand that it’s really no different than traditional asset management. Just as portfolio managers follow a process and make decisions based on certain factors and inputs—be it the level of risk or business fundamentals—enterprise data management should impart similar protocols. The governance policy, for instance, will spell out how the data is supposed to be managed, the checks and balances that are in place, and who has the ultimate authority to make decisions about the data. Whether it’s reference data, trading data, pricing data, et cetera, it can sound like a cliché, but organizations need to manage it like they would any other asset. And when that mentality becomes baked into the culture—parallel to technological advances that facilitate the organization’s ability to access and analyze data—this is when the data can really work for the organization. Enterprise data management can then empower capabilities such as predictive analytics, support executive decision-making, or enable true “big data” applications. But governance is key and represents a gating item as organizations begin their transformation journeys.

Q: There has also been an evolution in terms of how organizations pursue transformation. It’s no longer just about the technology. What are some of the themes that you’re seeing that may be different than past eras?

A: This new era bears very little resemblance to the past. The organizational complexity, globalization, the volume of data, and the technological advances we’ve seen in such a short window of time mean that there’s no single blueprint for CIOs to follow anymore. Each organization is unique and every asset manager is going to define their value proposition differently, which influences the makeup of the back office and the functions CIOs want to prioritize.

There is a school of thought that CIOs can’t scale their influence in an organizations if they’re trying to control everything and operate every necessary function in house. Technology, in and of itself, used to be viewed as a competitive differentiator. But today, you can’t have all of those systems running on internal servers and corral the resources to maintain, improve and run everything efficiently in a way that prevents bottlenecks. So CIOs today are prioritizing the capabilities that support the value proposition of their firm and looking to outsource commoditized functions entirely or enhance their capabilities through managed services. They’re effectively using these transformation initiatives as an opportunity to improve their operating model. And the cloud has been critical to help open new doors and provide CIOs with a blank canvas to reimagine their back office and how to successfully leverage technology, automation, and new data capabilities to add material value across the global enterprise.

Fund accounting, for instance, doesn’t provide a differentiator for fund managers. Plus, it’s resource intensive, and the largest firms have to staff a multitude of accountants. This drains internal bandwidth to recruit, train and manage this function, which, by the way, is constantly changing as the tax code evolves and as regulations change. So, increasingly, institutions are turning to a company like BNY Mellon that can absorb this function altogether. Managed services, meanwhile, has seen much more interest because it’s not about outsourcing commoditized functions, necessarily; it’s more about tapping focused providers who can augment a function and bring deep domain expertise and technology skillsets to bear. It becomes an extension of the organization that can deliver improved outcomes, particularly around data management, that would not be otherwise available in house.

The point, though, is that transformations look a lot different today because it’s about striking the right balance between technology and services. And with companies like Eagle and BNY Mellon investing in R&D and developing a cloud native deployment model, asset managers can feel comfortable that they have a trusted relationship with an industry leader—one that can bridge their organization to the future and deliver the required agility for asset managers to focus on their core competencies. As their own cloud strategy evolves and as their infrastructure becomes more mature, they can also take back certain functions into their own private or public cloud. CIOs simply have more options in this new era of digitization than they’ve ever had before.

Q: So in the absence of a one-size-fits-all model, what are the most important variables that will inform how an organization pursues transformation?

A: It all starts with the data. That has to be the first order of business. Who’s consuming the data; how is it being used; what are the sources; how is the data being governed; and, critically, can the larger enterprise really trust the quality of the data? Answering these questions and then resolving any shortcomings has to be the first place to start. Whatever the data set is, there must be one golden source. This is true for pricing data, transaction data, reference data, and an ABOR or IBOR—there can’t be any discrepancies or duplicative data sets.

The biggest challenge in the past, which became endemic to the data silos that were so common, was that so much of the data was being replicated in multiple places. So you could have two reports with conflicting numbers that involve the same data sets, produced on the same day. Of course, this erodes trust. But more importantly, it drains resources because the enterprise data management function then becomes a task focused on remediation versus contributing analysis or new capabilities that add material value to the business. Focusing on the data too can go a long way in helping organizations eliminate redundant systems, which improves operational efficiencies and cuts costs. Given where technology is going, though, clean, validated data is going to be necessary to take advantage of newer technologies—such as robotics and machine learning—and is just as critical in managing risks that are far more pronounced when there isn’t a rigid enterprise data management platform and process in place.